In financial industries such as the banking industry or the brokerage industry, a bank or broker provides business opportunities to its customers. In banking, the main business includes financial transactions between the bank as a banking business provider and its customers. However, there may be financial transactions conducted between customers themselves. These customer-to-customer (C2C) transactions may be called third party business activities because the provider bank is not financially involved in such activities. A group of customers connected through C2C business activities may be called a third party network. Better methods of understanding C2C transactions in groups and third party networks could help financial institutions identify new business opportunities and solve C2C business problems due to illegal group activities, such as money laundering activities and other group related frauds.
One approach to solve such financial problems in a database is data mining. There are two conventional approaches to study or understand transactions using data mining. One is an individual approach, in which each transaction and each customer are analyzed, and patterns associated with individual customers may be found. However, this approach does not provide any analysis of group patterns. Another approach is a group approach such as link analysis. Link analysis is a visual data-mining algorithm that helps to visualize connections between entities linked through transactions or other types of business activities. In comparison with the individual approach, link analysis shows the relationships and connections between individual entities within a linked group or network.
However, conventional link analysis approaches present several disadvantages. A third party network, i.e., one defined by a group of customers connected through C2C business activities, typically has at least two types of network properties. One is an internal property describing interactions and connections between member customers in a network. Link analysis is an adequate technique for analyzing and understanding the internal property of a link network. Another type of network property is an external property describing interactions and connections between a network (as a group object just like an individual customer) and other external entities such as a banking business provider. Under the existing link analysis techniques, external properties or characteristics of a link network are not apparent. Thus, the prior art presents no reliable way to understand and solve third party business problems, such as money laundering, thus allowing group patterns to become evident.
To understand the external property of a link network in solution space, it is desirable to extend link analysis to third-party or customer-to-customer network analysis in which business transactions between individual customers within a network and the business provider may be treated as transactions between a network object and the business provider. For example, financial transactions between individual members of a money laundering network and a bank should be treated as transactions between the network and the bank.